Zeitschriftenaufsätze

Multidimensional channel sounding measures the geometrical structure of mobile radio propagation. The parameters of a multipath data model in terms of directions, time-of-flight, and Doppler shift are estimated from observations in frequency, time, and space. A maximum likelihood estimation framework allows joint high resolution in all dimensions. The prerequisite for this is an appropriate parametric data model that represents the multipath propagation correctly. At the same time, a device data model is necessary that typically results from calibration measurements. The used model should be as simple as possible, since its structure has a considerable effect on the estimation effort. For instance, the inherent effort in parameter search is reduced if the influence of the parameters is kept independent. Therefore, the data model is characterized by several approximations. The most important is the “narrowband assumption,” which assumes a low relative bandwidth and also avoids considering any frequency response in magnitude and phase. We extend the well-known multidimensional Richter maximization approach (RIMAX) parameter estimation framework by including proper frequency responses. The advantage reveals itself with high bandwidth in the mmWave and sub-THz range. It allows for a more realistic modeling of antenna arrays, and it breaks with the usual narrowband model and allows a better modeling of mutual coupling and time delay effects. If the interacting object extends over several delay bins (hence, an extended target in radar terminology), we propose a model that assigns a short delay spread and a frequency response to the propagation path that associates it with the respective object. We verify the validity of the device model by numerical experiments on simulated and measured antenna data and compare it with RIMAX. In addition, we use synthetic data based on ray-tracing results and measurements both ranging from 27.0 to 33 GHz with known ground-truth information and show that the proposed estimator delivers better performance for higher relative bandwidths than the conventional RIMAX implementation.

The extension of low-earth orbit (LEO) services to non-terrestrial mobile communications has huge potential for eliminating network white spots and providing high-speed, low-latency links with worldwide geographic coverage. State-of-the-art user terminals for mobile platforms are too large for integration into a passenger vehicle. Antenna elements loaded with a dielectric superstrate could potentially lead to a considerable miniaturization of the user terminal. As per link budget calculations, an array with a gain of 27 dBi is necessary to ensure a throughput of 25 Mbps in the downlink at the Ku-band. A conventional array with a gain of 6 dBi per element, assuming a 12 × 12 arrangement with half-wavelength spacing, would require a footprint of 36 λ2 at 10 GHz to achieve this target and appears unsuitable for automotive integration. This paper proposes a low-profile, dual-band, dual-polarized, vertically stacked patch antenna with superstrate loading and shows that the inclusion of the superstrate improves the antenna’s gain by at least 3 dB. Therefore, compared to a conventional array, a superstrate-loaded array would need only half of the number of elements to meet the target gain, thus occupying only half of the surface area, and offers better integration for automotive applications. Requiring half of the number of elements also implies considerably reduced design complexity and cost.

Angiography is a very informative method for physicians such as cardiologists, neurologists and neuroscientists. The current modalities experience some shortages, e.g., ultrasound is very operator dependent. The computerized tomography (CT) and magnetic resonance (MR) angiography are very expensive and near infrared spectroscopy cannot capture the deep arteries. Microwave technology has the potential to address some of these issues while compromising between operator dependency, cost, speed, penetration depth and resolution. This paper studies the feasibility of microwave signals for monitoring of arteries. To this aim, a homogenous phantom mimicking body tissue is built. Four elastic tubes simulate arteries and a mechanical system creates pulsations in these arteries. A multiple input multiple output (MIMO) array of ultra-wideband (UWB) transmitters and receivers illuminates the phantom and captures the reflected signals over the desired observation time period. Since we are only interested in the imaging of dynamic parts, i.e., arteries, the static clutters can be suppressed easily by background subtraction method. To obtain a fast image of arteries, which are pulsating with the heartbeat rate, we calculate the Fourier transform of each channel of the MIMO system over the observation time and apply delay and sum (DAS) beamforming method on the heartbeat rate aligned spectral component. The results show that the lateral and longitudinal images and motion mode (M-mode) time series of different points of phantom have the potential to be used for diagnosis.

The block-term decompositions (BTD) represent tensors as a linear combination of low multilinear rank terms and can be explicitly related to the Canonical Polyadic decomposition (CPD). In this paper, we introduce the SECSI-BTD framework, which exploits the connection between two decompositions to estimate the block-terms of the rank-(Lr, Lr, 1) BTD. The proposed SECSI-BTD algorithm includes the initial calculation of the factor estimates using the SEmi-algebraic framework for approximate Canonical polyadic decompositions via SImultaneous Matrix Diagonalizations (SECSI), followed by clustering and refinement procedures that return the appropriate rank-(Lr, Lr, 1) BTD terms. Moreover, we introduce a new approach to estimate the multilinear rank structure of the tensor based on the HOSVD and $k$-means clustering. Since the proposed SECSI-BTD algorithm does not require a known rank structure but can still take advantage of the known ranks when available, it is more flexible than the existing techniques in the literature. Additionally, our algorithm does not require multiple initializations, and the simulation results show that it provides more accurate results and a better convergence behavior for an extensive range of SNRs.

In this article, a general introduction to the area of sensor array and multichannel signal processing is provided, including associated activities of the IEEE Signal Processing Society (SPS) Sensor Array and Multichannel (SAM) Technical Committee (TC). The main technological advances in five SAM subareas made in the past 25 years are then presented in detail, including beamforming, direction-of-arrival (DOA) estimation, sensor location optimization, target/source localization based on sensor arrays, and multiple-input multiple-output (MIMO) arrays. Six recent developments are also provided at the end to indicate possible promising directions for future SAM research, which are graph signal processing (GSP) for sensor networks; tensor-based array signal processing, quaternion-valued array signal processing, 1-bit and noncoherent sensor array signal processing, machine learning and artificial intelligence (AI) for sensor arrays; and array signal processing for next-generation communication systems.